Modern perception systems in the field of autonomous driving rely on 3D data
analysis. LiDAR sensors are frequently used to acquire such data due to their
increased resilience to different lighting conditions. Although rotating LiDAR
scanners produce ring-shaped patterns in space, most networks analyze their
data using an orthogonal voxel sampling strategy. This work presents a novel
approach for analyzing 3D data produced by 360-degree depth scanners, utilizing
a more suitable coordinate system, which is aligned with the scanning pattern.
Furthermore, we introduce a novel notion of range-guided convolutions, adapting
the receptive field by distance from the ego vehicle and the object's scale.
Our network demonstrates powerful results on the nuScenes challenge, comparable
to current state-of-the-art architectures. The backbone architecture introduced
in this work can be easily integrated onto other pipelines as well.

该研究提出一种新的方法，利用适合扫描模式的坐标系来分析自动驾驶中 LiDAR 传感器所获取到的 3D 数据，并引入了基于范围的卷积方法，其能够根据自我车辆与物体的距离和物体的比例来调整感受野。实验结果在 nuscenes 竞赛上表现良好，可以与现有的先进架构相媲美，而该架构也可以轻松地整合到其他管道中。

围绕你的一切：基于范围引导的圆柱形网络用于 3D 物体检测

It's All Around You: Range-Guided Cylindrical Network for 3D Object  Detection

A common approach to medical image analysis on volumetric data uses deep 2D
convolutional neural networks (CNNs). This is largely attributed to the
challenges imposed by the nature of the 3D data: variable volume size, GPU
exhaustion during optimization. However, dealing with the individual slices
independently in 2D CNNs deliberately discards the depth information which
results in poor performance for the intended task. Therefore, it is important
to develop methods that not only overcome the heavy memory and computation
requirements but also leverage the 3D information. To this end, we evaluate a
set of volume uniformizing methods to address the aforementioned issues. The
first method involves sampling information evenly from a subset of the volume.
Another method exploits the full geometry of the 3D volume by interpolating
over the z-axis. We demonstrate performance improvements using controlled
ablation studies as well as put this approach to the test on the ImageCLEF
Tuberculosis Severity Assessment 2019 benchmark. We report 73% area under curve
(AUC) and binary classification accuracy (ACC) of 67.5% on the test set beating
all methods which leveraged only image information (without using clinical
meta-data) achieving 5-th position overall. All codes and models are made
available at this https URL

本文阐述了一种针对 3D 医学图像数据分析时 2D CNNs 只能处理每个切片数据但丢失深度信息的问题进行的探索，提出了一些体积均一化方法，通过显著的不同性能提高，在 ImageCLEF 结核病严重性评估中实现了最高 73% 的曲线下面积（AUC）和 67.5% 的二元分类准确度 (ACC)。